With the latest advancements in artificial intelligence (AI), achieving human-like intelligence is gradually transitioning to the realm of possibility. And with disruptors like the Covid-19 pandemic ravaging the global economy, the race to achieve artificial general intelligence may have sped up significantly.
I would like to share some insights on the challenges and opportunities intertwined with the path to achieving AGI and the future ahead.
What is artificial general intelligence?
Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn and apply their intelligence to solve problems much like humans would for a given situation.
The anthropomorphic capabilities that convert artificial intelligence into artificial general intelligence include:
• Sensory perception.
• Fine motor skills.
• Natural language processing and understanding.
• Problem-solving.
• Social and emotional engagement.
• Creativity.
In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being. And based on these external inputs, they can discover solutions to an ongoing problem.
Challenges In The Way Of Artificial General Intelligence
While AGI may not have been realized so far, it promises a world of fruitful possibilities. However, it is currently plagued with serious roadblocks, which are present in the form of the following:
• The lack of a working protocol to help with artificial intelligence or machine learning networking is problematic. This deficiency coerces systems to work as standalone models in a closed environment. And such a mode of operation is a stark contrast from the convoluted and highly social “human experience.”
• Communication gaps come in the way of seamless data sharing and the inter-learning of machine learning models, which reduces universality.
• The absence of an artificial intelligence network also hinders the overall development of a common goal.
• Organizational executives are in the dark on how to integrate AI with their business operations to drive specific results.
• The lack of direction, complemented by the fact that companies cannot afford to hire a dedicated team of AI experts, makes the implementation of AI platforms costly.
• AI developers and companies often experience issues while selling their code and services.
How Can AGI Be Created?
There are three important goals that should be achieved in order to potentially create AGI.
1. We must connect companies in need of AI technologies with developers looking for monetization opportunities, which is made possible through an AI marketplace.
2. We should start interconnecting AI services and networks to create data lakes that can power AGI. The interactions between various AI platforms will help develop universal machine learning solutions.
3. We can begin democratizing access to AI technologies and challenging oligopolies to offer technologically advanced solutions for all.
These three goals can be achieved by setting up communication protocols for data and service exchanges, while also making AI more accessible through an end-to-end AI marketplace. The former helps to potentially lay the groundwork for AGI, while the latter connects companies and developers to reduce time to market.
Final Thoughts
The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030. Furthermore, advancements in robotic approaches and machine algorithms, paired with the recent data explosion and computing advancements, will serve as a fertile basis for human-level AI platforms.
Now, it is only a matter of time until AGI becomes a part of the new normal.
This article was originally posted by FORBES
